{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "6ed02522-8804-4c82-b77e-bdc31d2b4b04",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-01T08:16:12.605027Z",
     "iopub.status.busy": "2024-05-01T08:16:12.604746Z",
     "iopub.status.idle": "2024-05-01T08:16:13.367895Z",
     "shell.execute_reply": "2024-05-01T08:16:13.367273Z",
     "shell.execute_reply.started": "2024-05-01T08:16:12.605003Z"
    }
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cdb5fd31-b5cf-4ca5-8d57-c6f23feee419",
   "metadata": {},
   "outputs": [],
   "source": [
    "R_QDWT = [0.0, 0.1037, 0.1547,0.347, 0.817, 0.872, 0.892, 0.941,0.961, 0.976, 0.987,  1.0]\n",
    "P_QDWT = [1.0, 1.0, 1.0, 1.0, 1.0,  0.985, 0.97, 0.922, 0.686, 0.545, 0.342, 0]\n",
    "R_NMF = [0.0529, 0.65, 0.705, 0.758 , 0.800,  0.834, 0.870, \n",
    "          0.941, 0.9823529411764705, 1.0]\n",
    "P_NMF = [1.0, 1.0, 0.923, 0.784, 0.655, 0.563, 0.473,\n",
    "          0.192 , 0.1 ,0]\n",
    "R_QDCT = [0.152, 0.335, 0.558, 0.705, 0.852, 0.929,\n",
    "          0.970, 1.0]\n",
    "P_QDCT= [1.0, 1.0, 1.0, 0.983, 0.575, 0.305,\n",
    "         0.1872, 0]\n",
    "R_DWT = [0.065, 0.276, 0.453, 0.618,\n",
    "         0.753, 0.794,  0.808, \n",
    "         0.824, 0.853, 1.0]\n",
    "P_DWT = [1.0, 1.0, 0.949, 0.808,\n",
    "         0.452, 0.28, 0.221, \n",
    "         0.188,  0.133,  0]\n",
    "R_QSVD = [0.065, 0.212, 0.235, 0.488,\n",
    "           0.635,0.71, 0.807, 0.833,\n",
    "          0.853, 0.871, 0.929,\n",
    "          0.984, 1.0]\n",
    "P_QSVD = [ 1.0, 1.0, 1.0, 1.0, \n",
    "          0.938, 0.72, 0.518, 0.4,\n",
    "          0.314, 0.253, 0.155,\n",
    "          0.048, 0]\n",
    "R_QDFT = [0.094, 0.535, 0.759, 0.859, 0.892, 0.925,\n",
    "          0.955, 1.0]\n",
    "P_QDFT = [1.0, 1.0, 1.0, 0.95, 0.89, 0.68,\n",
    "          0.439, 0]\n",
    "\n",
    "R_GLCM = [0.065, 0.247, 0.512, 0.711, 0.794, 0.818,\n",
    "          0.853, 0.912, 0.976, 1.0]\n",
    "P_GLCM = [1.0, 1.0, 1.0, 0.985, 0.965, 0.865,\n",
    "          0.75, 0.436, 0.175, 0]\n",
    "R_Gloabl_local = [0.065, 0.247, 0.612, 0.771, 0.794, 0.818,\n",
    "                  0.853, 0.912, 0.956, 1.0]\n",
    "P_Gloabl_local = [1.0, 1.0, 1.0, 1.0, 1.0, 0.965,\n",
    "                  0.85, 0.496, 0.275, 0]\n",
    "R_list = [R_DWT, R_QSVD, R_NMF, R_QDCT, R_GLCM, R_Gloabl_local, R_QDFT, R_QDWT]\n",
    "P_list = [P_DWT, P_QSVD, P_NMF, P_QDCT, P_GLCM, P_Gloabl_local, P_QDFT, P_QDWT]\n",
    "name_list = [\"DWT\", \"QSVD\", \"NMF+Ring\", \"QDCT\", \"GLCM+DCT\", \"Global+Local\", \"QDFT\", \"Ours\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1b044460-e290-4c6d-a06f-08e868a7c0e4",
   "metadata": {},
   "outputs": [],
   "source": [
    "figure, ax = plt.subplots(1,1)\n",
    "colors = [\"#EC7C44\",\"#7FAC3A\",\"#78438A\",\"#1511DA\",\"#FA17FA\",\"#F1BB43\",\"#4AC2E8\",\"#EC2728\"]\n",
    "markers = ['o', 's', '<',  'v','D', '*', '+','^']  # 不同符号列表\n",
    "for R, P, name, color, mark in zip(R_list, P_list, name_list, colors, markers):\n",
    "    ax.plot(R, P, label = name, marker=mark, color = color, markerfacecolor='none', markersize=8,lw = 0.75)\n",
    "plt.grid(color = \"lightgrey\")\n",
    "ax.set_xlim(-0.005,1)\n",
    "ax.set_ylim(0,1.03)\n",
    "plt.legend(loc = \"lower left\",fancybox = False, fontsize = 10, edgecolor = \"black\", bbox_to_anchor = (0.04, 0.04))\n",
    "# plt.legend(loc=\"lower left\",bbox_to_anchor=(0.06,0.06),\n",
    "#           fancybox =False, edgecolor='black')\n",
    "plt.xlabel(\"recall\", fontsize = 14)\n",
    "plt.ylabel(\"precision\", fontsize = 14)\n",
    "ax.tick_params(axis='both', labelsize=10)\n",
    "plt.savefig(\"./P-R图.png\", dpi = 1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "5008af08-0482-4307-a681-e296970082b5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-01T08:16:15.149450Z",
     "iopub.status.busy": "2024-05-01T08:16:15.149168Z",
     "iopub.status.idle": "2024-05-01T08:16:17.610449Z",
     "shell.execute_reply": "2024-05-01T08:16:17.609926Z",
     "shell.execute_reply.started": "2024-05-01T08:16:15.149429Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "figure, ax = plt.subplots(1,1)\n",
    "x_r = [0, 0.07090909, 0.12181818, 0.18545455, 0.21272727, 0.24272727,\n",
    "       0.29454545, 0.33636364, 0.4227273, 0.57818182, 0.78818182,\n",
    "        1]\n",
    "x_p = [0, 0.07090909, 0.12181818, 0.18545455, 0.22272727, 0.26363636,\n",
    "       0.29454545, 0.33636364, 0.4227273, 0.64818182,\n",
    "       0.80909091, 1]\n",
    "R_QDWT = [0, 0.01, 0.10,0.4, 0.78, 0.942, 0.985,0.993, 0.994, 0.995, 0.997,  1.0]\n",
    "P_QDWT = [1.0, 1.0, 0.99, 0.972, 0.936, 0.855, 0.712, 0.449,0.129,0.014,0.003,0]\n",
    "ax.plot(x_r, R_QDWT, color = \"#7FAC3A\", marker = \"o\", markerfacecolor='none', markersize=6,lw = 0.75)\n",
    "ax.plot(x_p, P_QDWT, color = \"#1511DA\", marker = \"*\", markerfacecolor='none', markersize=6,lw = 0.75)\n",
    "ax.set_xlim(-0.01,1.01)\n",
    "ax.set_ylim(-0.01,1.01)\n",
    "plt.xlabel(\"recall\", fontsize = 14)\n",
    "plt.ylabel(\"precision\", fontsize = 14)\n",
    "ax.tick_params(axis='both', labelsize=10)\n",
    "plt.grid(color = \"lightgrey\")\n",
    "plt.savefig(\"阈值图.png\", dpi = 1000)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.16"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
